Out of 656 errors due to the scope, the conjunction particle "-to" follows 126 events, "ga" follows 78 events, "-te" follows 70 events, and so on.

when we detect scope in practice, we assume to use conjunctive particles as the key to determine propagation boundaries.


However, the issue regarding scope includes the issue by profound meaning and context.

we consider that this issue is high-priority challenge.


The window slides one word per step from the beginning of each sentence to the end.

the 3-length and 6-length windows capture two and five words at most on each side of the target word respectively.


We deem that semantic relationship is a more direct clue of a word's meaning than either its textwindow co-occurrences or syntactic dependencies.

we introduce a semantics layer upon the distribution layer to exploit semantic relations.


With the 3-length window, the performance of PMI, Jaccard, and LMI are comparable.

we will have to try out all PMI, LMI and Jaccard in the fusion phase.


FUSE uses top 10 relation words to calculate the relation-based similarity.

instead of examining the global extraction results, we focus on the top 10 relation words of the target words in our dataset, because FUSE's performance is our main concern.


This paper proposes a novel alignment scheme, named labeled alignment, to address this problem, which introduces negative links to explicitly mark the contradictory expressions to justify the non-entailment pairs.

the alignment-based RTE method employing the proposed scheme, compared with those employing the conventional one, can gain accuracy improvement through actively detecting the signals of non-entailment.


(1b), "ferry sinking", "cause" and "that" are all linked to NULL 3 , while only "ferry sinking" is the cause for non-entailment.


It is a very competitive result comparing with the CKIP system, which achieves precision 57.4% and recall 88.3%, and it is supported by an approximately 5-million Chinese-word database.

this study met the need of carrying out CWS in a novel corpus without appropriate dictionaries.


Hence, we chunked longdistance context samples into short-distance strings, uni-grams and bi-grams, for feature vector extraction.

we could collect longdistance context information without dealing with the probability sparsity problem.


This feature set is similar to the one used in the Stanford POS tagger, but our tagger does not include some hard coded treatment and rules (e.g., bidirectional transition rules) used by the Stanford tagger.

the performance of our tagger is slightly lower than the Stanford tagger.


Real abbreviations contain much less word units.

assumption-2 will have the inconsistency problem between abbreviations generated from NFFs and real abbreviations.


Thus, assumption-2 will have the inconsistency problem between abbreviations generated from NFFs and real abbreviations.

assumption-2 works worse than assumption-1 which gives no abbreviations.


As we can see, the up-bound of the micro-F-score and the macro-F-score is 79.81% and 81.04%, respectively.

the web search quality of automatic generalized abbreviation prediction still has a large space to be improved, possibly via a larger training data set in the future.


In the context of academic meetings the number of pauses may be indicative of the time a speaker takes to formulate and articulate his/her thoughts.

more pauses may indicate utterances that have been more carefully crafted and therefore include more relevant content.


The difference between the number terms and the number of words in a summary is that a single term can appear more than once in a sentence.

the total number of words in the text is expressed by summing up the elements of its term-count matrix.


Therefore, the total number of words in the text is expressed by summing up the elements of its term-count matrix.

maximality constraints for words are expressed by the following linear inequality.


Undoubtedly, manual compilation is the best way to create such an emotion lexicon but is much expensive in terms of time and human effort.

the objective of the present paper is to develop a method for automatically creating such a list of words from the glosses of a dictionary, as well as from a thesaurus and a corpus.


By sampling from this subset of non-relations, we safely approximate the non-relation background of the unlabeled data and foster the early learning of the entity type rules.

the queries will also be more meaningful to users even at the beginning of the active learning process.


Afterwards, we counted the occurrences of lexical items and rules.

1,000 samples are taken for each sequence.


It indicates that with a wide beam width the estimated value of ( ) may be significantly lower than the true value of ( ), while with a narrow beam width the estimated value of ( ) is likely to be similar to the estimated value of ( ), which may be significantly higher than its true value.

the success of the use of ( ) is dependent on the beam width.


that consists only of a stopword or contains q or is contained in q.

some noisy paraphrases are easily eliminated.


Research has shown that the act of searching for, appraising, and synthesising evidence from multiple documents generally requires more time than practitioners can devote (Ely et al., 1999).

practitioners would benefit from automatic systems that help perform these tasks and generate bottom-line recommendations.


For example, a bottom-line summary may or may not recommend an intervention in response to a disorder.

the bottom-line summaries can be considered to be polarised -when an intervention is recommended, the polarity is positive, and when it is not recommended, the polarity is non-positive.


We examined the causes for unrecalled interventions and found that of the 10 not recalled, 4 were due to missing abstracts from the corpus, and 2 drug names were not mentioned in any of the referenced abstracts.


Certain adjectives, and sometimes nouns and verbs, or their synonyms, are almost invariably associated with positive or non-positive polarities.

for each adjective, noun or verb in a sentence, we use Word-Net 9 to identify the synonyms of that term and add the synonymous terms, attached with the 'SYN' tag, as features.


We noticed that, in a sentence, the words in the vicinity of the context-intervention may provide useful information regarding the polarity of the sentence relative to that drug.

we collect the terms lying inside 3-word boundaries before and after the context-drug term(s).


We showed that the same sentence can have differing polarities, depending on the context intervention.

incorporating context information in the form of features can be vital for accurate polarity classification.


Twitter, for example, generates over 400 million tweets per day in early 2013.

it is crucial to develop efficient clustering algorithms that can handle such massive amounts of streaming data.


However, in a dynamic microtext stream, word vocabulary changes and the number of tweets increases over time, making it computationally expensive to recalibrate the inverse document frequency of TF-IDF.

we resort to using term frequency as the term weight and adopting a sparse matrix representation of tweets to deal with dynamically changing vocabulary in our clustering algorithm.


Since real-world events typically span a limited time interval, tweets that largely differ on their publication times are much less likely to belong to the same event.

in order to cluster tweets into temporally-related groups, we also exploit a time similarity measure defined as which is based inversely on the distance between tweets' publication dates/times.


For a particular event, since users tend to convey the same or a similar meaning using different words depending upon their own personal style, the online phase would organize the tweets that report the same event, but expressed using different words, into different base clusters.

we propose to merge together the clusters that are related with respect to the same event in the offline phase.


Generally, the navigational bars locate in one block or several blocks of a web page.

it is necessary to split the web page into blocks for identifying the navigational bars.


Content Extraction: Most of the methods for content extraction try to use different threshold selection strategies for different features, but the generalization performance of these algorithms are still not satisfactory.

we adopt the K-means clustering with two-dimensional features to get the final content.


To the best of our knowledge, there is no publicly available Vietnamese questions corpus.

of this work, we will share our newly built corpus to the research community.


For this work, we consider precision to be more important than recall because a bot that frequently answers questions incorrectly could anger chat users and cause them to ignore the bots.

f 0.5 is more appropriate here than the standard f score because it places more emphasis on precision.


We assume each individual sentence of the course material as the candidate extract sentence and calculate its ROUGE similarity scores with the corresponding golden essay.

an average ROUGE score is assigned to each sentence of the course content.


Since the maximum value of the second term in WMSS is 1 |W | and maximum number of stems in any T s (splitset) is less than |W |, |T s (splitset)| < t∈Ts(splitset) wg(t) < |T s (splitset)| + 1.

any solution of WMSS should be a solution of MSS, but the reverse is false.


Furthermore, the learned models suggest strong correlations between financial sentiment words and risk of companies.

these findings are of great value for providing us more insight and understanding into the impact of financial sentiment words in financial reports.


As shown in Section 4.1, the ranking models can obtain more consistent results than the regression ones.

in the following discussions, we conduct some analyses on the words learned from the ranking models.


We then discuss the term "deficit" from the Fin-Neg list, which means an excess of liabilities over assets, of losses over profits, or of expenditure over income in finance.

it is natural to say that a company associated with higher deficit might  and Only Sentiment Words (SEN).


In addition, the learned models also suggest strong correlations between financial sentiment words in financial reports and the risk of companies.

these findings provide us more insight and understanding into the impact of financial sentiment words on companies' future risk.


As mentioned above, we start with an event coreference resolver that assumes that all the six upstream components are error-free (see row 1 of Table 1 for its performance), and then replace each oracle component with its system (i.e., machinelearned) counterpart one after the other (rows 2--7 of Table 1).

the results in the last row of Table 1 correspond to the performance of an end-to-end event coreference resolver that relies solely on system components.


In comparison to row 2b, we see that there is a slight, but insignificant, improvement in coreference performance.

we assume in the rest of our experiments that we employ system event mention attribute values on both the training set and the test set (i.e., the configuration in row 2c), since it seems to more accurately reflect the impact of event mention attributes on event coreference performance.


The form of a translation template is similar to that of the hierarchical phrase pair rule (Chiang, 2005), except that the translation template is designed for translating unknown words, whereas the hierarchical phrase pair rule is designed for translating phrases.

they differ in terms of the training process and rule fitting process.


The point is that, most of the known Kanji characters are annotated to be notional words such as nouns.

cabocha, which takes words/characters and their POS tags as features for discriminative training using a SVM model (Kudo  and Matsumoto, 2002b), can still correctly tend to include these single-Kanji-character words into one chunk.


Consequently, Cabocha, which takes words/characters and their POS tags as features for discriminative training using a SVM model (Kudo  and Matsumoto, 2002b), can still correctly tend to include these single-Kanji-character words into one chunk.

we can re-combine the wrongly separated pieces into one (compound) word.


However, the best performing system at TREC-10 QA track used only an extensive list of surface patterns (Soubbotin and Soubbotin, 2001).

we have decided to investigate their potential for Turkish factoid question answering.


We want to retrieve documents that contain many sentences holding the question phrase and answer phrase together.

there is a bigger chance that our answer patterns match those sentences, and the correct answer can be extracted.


In this paper, typical core parts of VSM are applied to measure semantic similarity over documents.

instead of using raw frequencies tf-idf weighting is adopted and length normalization is performed on both queries and target documents (Salton and Buckley, 1988;Buckley, 2005).


Only four of the 30 queries showed higher MAP scores for the word-based separation method.

in this study, the morpheme-based approach significantly outperforms the word-based approach.


In Table 1, the notation [+/−] indicates whether the function was employed in the workflow of the experiment.

keyword extraction with plus sign means that the PageRank-based ranking algorithm was used for the keyword extraction process.


One keyword missing may lead the change of the tweet's classification result.

we adopt character unit as feature to allow the mistake of spelling in some extent.


Performances of semi-supervised methods and other systems The task is to classify the tweets related or non-related with the given organization, it belongs to classification task.

we measure the performance by accuracy, precision, recall and F-measure.


Feature A: Because an evaluative condition modifies the evaluation expression, they are usually in close proximity to each other.

there should be a pass of dependencies between an Iphrase and the evaluation expression, and a phrase in closer proximity to the evaluation expression is more likely to be an I-phrase.


We used the two-tailed paired t-test for statistical testing and found that the differences of "Rule" and "All" in F-measure for "Phrase" and "Condition" were significant at the 1% level.

we conclude that each of our five features was independently effective for extracting evaluative conditions in review sentences and that when used together the improvement was even greater.


On the basis of the input data, the Moses training process builds up a phrase table consisting of character sequences in our case.

of the training process, we receive a cognate model that can be used to produce cognates in the target language from a list of input test words.


We find that bigrams produce slightly better results than unigrams and trigrams, this is in line with findings by Nakov and Tiedemann (2012).

in the following experiments, we use character bigrams.


However, the test dataset contains a variety of cognates following many different production processes.

we evaluate the effect of the size of the training data on COP.


As expected, both coverage and MRR improve with increasing size of the training data, but we do not see much improvement after about 1,000 training instances.

cOP is able to learn stable patterns from relatively few training instances.


However, even a list of 1,000 cognates is a hard constraint for some language pairs.

we test if we can also produce satisfactory results with lower quality sets of training pairs that might be easier to obtain than a list of cognates.


As the similarity measures often assign the same value to several candidate pairs, we get many pairs with tied ranks, which is problematic for computing coverage and MRR.

we randomize pairs within one rank and report averaged results over 10 randomization runs.


For crossalphabet pairs, this is not possible.

the task is to tackle both, standard transliteration (as in the English-Greek pair atlas-άτλας) 9 and cognate production (as in archangel-αρκάγγελος) 10 .


The number of training instances is rather small, as a language reform in the 19th century eliminated many Czech words with Austrian or German roots.

the model does not generalize as well as for other language pairs (see the column "COP Productions" in Table 6).


also represented a question in the QC task similar to document representation in vector space model, i.e., a question is a vector which is described by the words inside it.

a question Q can be represented as: and N = total number of Terms We have also used three types of features for QC.


Thus our target semantic labeling set comprises 27 categories including business, environment, health, pets, sports and travel.

we are able to completely automatize our approach without the need of manually annotated training material, and to automatically create a huge annotated corpus of semantically labeled question-like search queries.


As a rule of thumb, most frequent queries in search logs are navigational (Broder, 2002;Rose and Levinson, 2004).

we used all search queries having a frequency higher than 1,000 across our session corpus as additional transaction splitters.


In this case, it is obvious that the phrase "想 一 想" (think) can only be associated to rules with VCD but not to verb phrases (VP), which limits its usage.

a wider coverage of rules is available during the decoding process.


However, not all reader feedback responds to the article.

we screen reader feedbacks in Step (1) based on the article ART, its feedbacks FB and interest preference scores IntPrefs.


Weakly labeled data are noisier than manually labeled data, which might cause the classifier to be less generalizable due to overwhelming noisy features.

we perform feature selection by using the Bayesian Information Criterion (BIC) to reduce noise and improve the performance (Schwarz, 1978;Forman, 2003).


Unlike bootstrapping, our approach trains the classifier only once.

classifiers that take long to train such as Support Vector Machine can be trained within reasonable amount of time.


The sememes are also described by DEF in HowNet2008.

sememe similarity (Sim s (S 1 , S 2 )) can be divided into two parts, namely structure similarity and DEF similarity.


Dependency parsing accuracy is always decisive for the performance of subsequent tasks.

reducing dependency parsing errors or selecting high quality dependencies is a primary issue.


When a word pair that contains a verbal phrase between them, the two words are always on different sides of a parent node.

these kinds of word pairs will always have no dependency link between them.


The IDG also provides a reasonable constraint on a user simulation model, which is used when learning a policy function in POMDP and dialogue evaluation.

this method converts a conventional dialogue manager to a statistical dialogue manager that utilizes task domain knowledge without annotated dialogue data.


Obviously, a reasonable assumption is that the target example has a higher confidence to occur in the final output if the corresponding source part of example has a higher similarity with the source input sentence.


To resolve place pronouns, we use dependency relations and Named entity Categories.

place pronoun can be resolved by selecting the noun phrase nearest to the pronoun which has 'LOCATION' as NER-Category or the nearest NP with the dependency label 'k7p' or 'k2p'.


However, there are differences between MSA and AD, they are considered as two related languages.

we suggest in this paper to exploit and adapt an MSA morphological analyzer (MA) to Tunisian Dialect (TD).


The refinement phase considers the overall strength of occurrence of each morpheme in the vocabulary.

if a certain root morpheme is a true morpheme then all the pattern morphemes it occurs with would have higher scores since they also would be true morphemes.


ST (L(N )) corresponds to the level of the node N j contained in the semantic tree ST .

we give the sense that corresponds to the semantic tree that obtains the highest score.


Both the POS model and the Persian verb analyzer/segmenter expect the input text to have standard semi-space usage.

we have to apply this step after semi-space correction.


In this work we focus on interoperability of two different types of frameworks.

this paper realizes interoperability between those two types of frameworks to mutually complement their disadvantages.


The main contributions of this paper are as follows: • The integration framework increases availability of language resources.

it facilitates the process of creating applications.


Other problems are the limited availability and size of lexical resources for languages other than English.

1 various previous works (Grefenstette, 1994) among others, suggest to acquire synonyms and other semantically related words automatically from a monolingual corpus.


For example, it is difficult to translate the Japanese word (hang, put, bring,...) because of its many different meanings depending on the context.

we combine the context vectors to a new context vector q * as follows: If a word x in language A is in the dictionary, we set otherwise we set c x ∈ [0, 1] is the degree of correspondence between word x and its translations in language B.


Since the failed sentences are not necessarily parallel in the source and translation sides, this could affect the downstream QE performance.

we opt to employ as our "lower-accuracy" models the second smallest training set sizes, which are 1K sentences for English and 500 for French.


Nowadays, the widespread use of Unicode is one of the reasons of text variant, particularly when it comes to text processing, because many NLP tools, e.g., syntactic parsers, cannot handle Unicode characters properly.

during many annotation projects, Unicode characters, e.g., Greek letters, are spelled out into ASCII alphabets, like beta in Figure 1 A.


Although many studies have proven that NBC with ULM can provide satisfactory performance, the method has a limitation: the classification process is based on literal term matching and only considers frequencies of words occurring in a class.

nBC with ULM usually suffers from the issue of word usage diversity and polysemy, which can degrade the DC performance.


But sentence context gives rise also to a clear decrease in average classifier performance.

sub, while significant, is less useful than the semantic features.


The equation above can be rewritten as Moreover, if we only improve the identical templates, the similarity matrix W T op f 2 and normalized matrix S T op f 2 degrade to identity matrices, the final iteration equation is There may be still noisy in the set of candidate NEs despite using the high-quality templates to find new entities.

we also need to filter out the non-NEs for the further processing to insure the high accuracy.


We think the words with initial positive scores are reliable, because they satisfy two strong constraints, namely alignment by IBM models and existence in the lexicon.

unlike (Munteanu and Marcu, 2006), we only apply the averaging filter to the words with negative scores.


External parallel data might be helpful for alignment models to detect parallel fragment candidates from comparable sentences.

we compared two different settings to investigate the influence of external parallel data for alignment to our proposed method: • Only: Only use the extracted comparable sentences.


In our application scenario, we plan to implement several different text manipulation methods (and combine them based on their confidence).

the goal of this study is to propose an approach with high precision and coverage will be ensured by a range of syntactic methods and paraphrasing together.


As can be seen, each cue is paraphrased with another cue with the same part of speech.

their inflected forms can be generated based on the original words' POS tags.


Consequently, Cabocha, which takes words/characters and their POS tags as features for discriminative training using a SVM model (Kudo and Matsumoto, 2002b), can still correctly tend to include these single-Kanji-character words into one chunk.

we can re-combine the wrongly separated pieces into one (compound) word.


Since all the probabilities are estimated in a maximum likelihood way, we can simply update the probabilities in these models by accumu-lating frequencies to similar words/phrases.

we say that our IME system is self-growing as the Web becoming larger and users using it longer.


It is better to disambiguate more words in earlier steps with higher accuracy since the ambiguous words will be disambiguated with less reliable heuristics unless we disambiguate them at earlier steps.

we select ST as the second, and MS as the third.


It is known that entropy is a measure of the uncertainty in a random variable (Ihara, 1993).

this uncertainty can be quantitatively determined by entropy(H) calculated by the following formula 2 http://www.maltparser.org/userguide.html#predstrate were p(y i /X) is the posterior probability of y i being predicted as the parsing action in the given context X.


It should be noticed that the Shift action will not add any edge, but will have a very complex effect on the set of edges that could or could not be added in the future.

the entropy of this particular decision cannot be attached to any specific final edge and hence the methodology can not be extendend to arc-attachments.


(2013) note that their tagger performs better on Germanic languages than that of Das and Petrov (2011), which might be because this is the same family as the source language used (English).

language relatedness is an important factor to consider.


In the case of to love, the s node is farther from the head than the vp node.


Unlike English and other western languages, Chinese do not delimit words by white-space.

word segmentation is a very basic and important pre-process for Chinese language processing.


Any automatic segmentation has limitations in some way and is far from fully matching the particular need of users.

human-computer interactive strategies are explored to allow users to pass their linguistic knowledge to the segmenter by directly intervening the segmentation process.


If the initial result does not conform to human judgments, more classes with smaller ranges should be generated.


In contrast, SCW-I used in Online is based on margin criteria, and ignores data outside the margin.


This means that on average a more frequent word does not differ as much in its frequency from a word 10 ranks further down in Polish as it does in English.

there will be fewer instances of the 100 most frequent words in the same portion of text.


We incorporate this into the pattern generation step of our URE method and use this information to model selectional restrictions.

the type system is not manually specified, but rather induced without supervision from a large Web corpus, making it a natural fit for the open domain and URE.


The focus on simple coordination patterns circumvents the need for identifying various syntactic relations (e.g., subject), that are part of the extraction patterns of the original Basilisk.


Apparently, the patterns that are selected in the beginning of learning as the ones best suited for identifying substances are all fragments of coordinations.

performance of Basilisk-G and Basilisk-C for a lexicon of 10,000 MWEs (cf.


The analysis of previous work has shown that there is almost no models for ISTERE.

to align with prior work, we compare with previous models on standard TERE (extraction within a sentence).


TKs are equivalent to a scalar product between vectors of tree fragments.

using TK in SVMs is equivalent to use subtrees as features.


We also show that exploiting syntactic information of the sentence containing the relational expressions is not trivial.

we developed a bag-of-words (BOW) tree representation capturing the context of the target time/event expressions.


In these experiments, the only critical parameter is the one tuning the Precision/Recall trade off (cost-factor parameter) as the high data imbalance between negative and positive examples can result in imbalanced Precision and Recall.

we plotted the F1 of the above models (derived on the test set) according to a reasonable set of values of such parameter.


Because the KB is incomplete, the annotated corpus may contain annotation mistakes; falsepositive and false-negative.

our method tries to filter out sentences that are likely to include those problematic annotations based on statistical measures and morpheme patterns induced from the entries in the KB.


The value of N is defined by the equation N = max(2, M S /100) where M S is the number of merchants providing structured data in the category.

we obtain a vector of attributes that can be regarded as synonyms, such as (region, country), and (grape, grape variety) for the wine category.


We detect incorrect value annotation in the corpus according to the assumption that attribute values with low MFs in structured data (i.e., tables and itemizations) and high MFs in unstructured data (i.e., product descriptions) are likely to be incorrect.

we designed the following score: Chateau Talbot is a famous winery in France .


One of the possible way to reduce the influence of the missing annotations is to discard sentences with missing annotations.

we removed such sentences from the corpus.


The second type of models, sequence models, is motivated by an observation: since a post in a post sequence is a reply to its parent post, its label should be determined in dependent relation to that of its parent.

these models assume as input a post sequence and output a sequence of stance labels, one for each post in the input sequence.


In general, entrance examinations are mostly described in natural language, and the goal of reading the text is clear, viz., to solve questions.

this is an ideal resource for evaluating end-to-end NLP systems that read natural language text, perform some information processing, and output answers.


They are carefully designed for empirically quantifying a certain ability of high-school-level students.

it is not a trivial task for NLP systems to solve university entrance examinations.


It is almost hopeless to invent a single clever algorithm that can solve all types of questions.

it is necessary to break down entire examinations into NLP subtasks that can be investigated solely.


An important point here is that, question classification allows us to extract individual NLP tasks, but, at the same time, their contributions to entire performance are always accessible.

our resource is inherently different from NLP resources that focus on monolithic NLP tasks/applications in nature, such as parallel corpora for machine translation research and evaluation data sets for question answering.


However, because questions must be solvable by high-school students, this task requires much smaller knowledge space than contemporary NLP applications such as Web-scale question answering.

we can focus on algorithms of problem solving rather than relying on huge data.


In some cases, they have to be connected to non-NLP components, such as mathematical solvers and ontology-based inference engines.

investigating entrance examinations promotes interdisciplinary research within NLP, as well as with outside of NLP.


This also indicates the difficulty of focused research on individual NLP technologies.

we provide annotations for question classification, which enables us to extract a subset of examinations that is relevant to focused NLP tasks (see Section 3 and 4).


1 Center Test is a nation-wide standardized test for university admission in Japan, and almost all high-school students who aim to enter a university in Japan take this exam.

questions are carefully designed in order to accurately quantify achievement levels of high-school students.


Although understanding of semantics of formulas is indispensable, the   semantic analysis of formulas is not trivial and is beyond the scope of NLP research.

we marked up all formulas that appear in examination texts with MathML.


The novelty of this pilot task is that questions are originally developed for assessing human English ability, rather than specifically developed for NLP system evaluation.

it is expected that various aspects of human natural language understanding appear in solving such questions.


Collaborative research is indispensable for our purpose, because entrance examinations involve a variety of NLP subtasks, and a single research group cannot solve the entire problem.

it is necessary to develop open resources as described in this paper.


In essence (with abuse of notation and terminology), damage and relief are independent variables without extra information, however, they are related given earthquake.

we would like to add the semantic edge between these events.


Given the pair of words (K i l , K j m ) (we will omit subscript l and m, when there is no ambiguity), we find the intersection of corresponding document lists.

at the end of this step we have list of documents (denoted by D lm ) in which both the words co-occur along with their frequency in the documents.


Those hidden variables are typically introduced in joint probabilistic models as: By inspecting Figure 1b, we can observe that readability score r is independent from observable linguistic features l i given the language constructs Lex, Syn and Sem.

we can rewrite Equation 4 to factorize over the graph in Figure 1b as:  l i+1 , .


However, those methods have difficulties in aligning cognitive effort spatially and temporally to segments in a text, and we opted to measure fixation time on individual words due to its relative simplicity.

we work under the assumption that higher cognitive effort is reflected as longer fixation durations, since parts of the text that are difficult to read require longer cognitive processing time.


Clearly, the larger the training corpus, the more representative it is likely to be, and thus the more reliable the statistics of words.

the number and distribution of types in the available small sample are not reliable estimators (Evert and Baroni, 2007).


To build our reference lists, we selected only the English/French pair of single-word terms (SWTs) which occur more than five times in each part of the comparable corpus.

of filtering, 321 English/French SWTs were extracted (from the UMLS 3 meta-thesaurus) for the breast cancer corpus and 100 pairs for the wind energy corpus.


In practical applications, PMI was found to have a tendency to assign inflated scores to lowfrequency word pairs.

even a single cooccurrence of two words might result in a fairly high association score.


In particular, news articles are more uniform in style and structure; whereas blogs often do not exhibit a stereotypical discourse structure.

for blogs, it is usually more difficult to identify and rank relevant units for summarization compared to news articles.


First, because it posits instances requiring particles, it also filters out instances that do not need a particle to be grammatical.

the particle choice classifier does not need to include NULL as a possible class, cutting down on training size and complexity.


There are hundreds of particles in the Korean language, but many of these are not used often, e.g., 9 particles cover 70% of particle use in a data set of thesis abstracts and 32 cover 95% in a study by Kang (2002).

the training data should only include particles which can reasonably be ex-pected to appear when the learner has omitted one.


It is more important to point out that the noticeably higher performance of orthographic errors suggests that these errors discriminate well between two language backgrounds within one level of proficiency.

this means that learners of two language backgrounds make errors specific for their L1 group.


For instance, using včera bude sněžit 'yesterday it will snow' instead of včera sněžilo results in a verb tense error, future form bude sněžit is used instead of the past snežilo.The two errors above largely concern Czech morpho-syntax.

greater differences between L1 and L2 grammatical structures might trigger a higher amount of errors within the NIE group.


Our analysis of the best performing features shows that learners of both language backgrounds within each level of proficiency produce errors that discriminate well and vary in nature between IE and NIE learners.

we cannot strictly follow the intuition that the NIE learners make more errors, although these results might change with a larger number of learner essays.


In Figure 3, we take FCWM to learn the CAR for each word (including OOV).

there is easy to use the proposed CCWM instead of the FCWM.


Even with an attention mechanism, the Seq2Seq model just pays attention to the current alignment information between the inputs and the output at the current position but ignores past alignments information.

it cannot keep track of the attention history when it updates information at each current time step, leading to the over-production (Tu et al., 2016a,c;Mi et al., 2016;Tu et al., 2016b).


That is, instead of predicting only words at each time step, the model trains the network to predict both words and their grammatical dependencies as a dependency tree at each time step.

it is hoped that the accuracy of output will be improved.


The problem is defined as follows: Given a source sequence , we want the model to encode the input sequence X and decode it to a tree structure with both words and dependency information conditioned on the encoded vector.

the output will be represented in the form (LY ) = (ly 1 , ly 2 , .


The conditional probability p(ly|x) is decomposed as: in which (ly 1 , ly 2 , ..., ly M ) are words or dependency labels.

the hidden state s j at time step j is computed as follows: and the next token ly j , which may be a word or dependency label, will be generated as follows: In this paper, dependencies are defined as the dependency labels which are achieved from the Stanford Dependency Parser (Chen and Manning, 2014).


From this section onwards, we will refer to the Seq2Seq and Seq2Dep models with sentences of maximum length 50 and 80 tokens as Seq2Seq-50, Seq2Dep-50, Seq2Seq-80 and Seq2Dep-80.

the BLEU score of Seq2Dep-50 with post-processing was 20.88, which is higher than the BLEU score of 19.31 obtained by the attention-based Seq2Seq-50 model with a gain of up to 1.57 points.


's work (Kuncoro et al., 2016) in which modeling of composition can achieve better performance, the Seq2Dep model which learns about the syntactic dependencies and tree structure performance is probably able to learn the blocks of the form "Nonterminal word }" like a phrase-structure in sen- Figure 6: Comparison of the repetition rate of the baseline and Seq2Dep models tences, so it is unlikely to generate the same word repeatedly.

it is possible to prevent the long repeated words in long sentences.


However, there is no work that specifically studies attention and provides analysis of what is being learned by attention models.

the question still remains that how attention is similar or different from the traditional alignment.


In addition, we conducted experiments using a large-scale Lang-8 corpus.

we showed that it is better to extract error patterns from such a corpus to train word embeddings than simply add Lang-8 corpus as a training data to train a classifier.


More-over, since LLGPar is a second-order model, the presence of certain dependencies can directly affect the choice of other dependencies through the scores of adjacent siblings.

given the same amount of annotated dependencies, random PA contains more syntactic information than random FA, which explains why LLGPar performs better with PA than FA.


Although word embeddings produced by these models have been shown to give improvements in a variety of downstream tasks, it still remains difficult for these models to learn syntactic word representations owing to their insensitivity to word order.

word embeddings produced by these order-insensitive models are thus suboptimal for syntax-related tasks such as parsing (Andreas and Klein, 2014).


As ∑ y α y is too large to be ignored, especially when the frequency n(x) is small, Equation (4) shows that the maximum likelihood estimationp y | x estimates the probability to be excessively large.

we propose using Kneser-Ney smoothing (Kneser and Ney, 1995), which is considered to be an extension of the Dirichlet smoothing above, to mitigate overestimation of paraphrase probability in bilingual pivoting.


To address the problem of frequent phrases, we smooth paraphrasability by bilingual pivoting in Equation 3using word probabilities p(e 1 ) and p(e 2 ) from a monolingual corpus that is sufficiently larger than the bilingual corpus.

we can interpret the bi-directional bilingual pivoting as an unsmoothed version of PMI.


Bilingual pivoting is robust against noise arising from antonym pairs, unlike distributional similarity.

s lpmi (e 1 , e 2 ) can perform paraphrase acquisition robustly by compensating the disadvantages.


system needs a selectional preference score representing the probability of a word w being the l argument of predicate p, P (w:l|p:P RED).

to convert our PRNSFM probabilities to selectional preferences, we need to marginalize over the possible argument sequences.


Intuitively, the tree enumerates all possible argument sequences that start with the predicate, have zero or more intervening arguments, and end with the word and label of interest, w:l. The probability of w:l given the predicate is the sum of the probabilities of all branches in this tree, i.e., of all possible sequences that end with w:l. In reality, we do not have the computational power to explore all possible sequences, so we must limit the tree somehow.

we only ask the PRNSFM for its top k predictions at each branch point, and we only explore sequences with a maximum length of T .


We manually adjudicated the latter two cases.

82% of the final labels in the dataset correspond to a majority vote over the judgments (the remaining 18% differ due to our manual correction).


KB contains known information for the models.

in this system, it includes the observed layer and the feature layer which contain gold relations and the features for the testing data respectively.


Predicates belong to the same frames are highly related to a specific situation defined for the frame.

it is intuitive to acquire lexical inference pairs from predicates in the same frame.


Moreover, Weeds' method generates high score when an infrequent lexicon has features similar to those of another lexicon, which harms the performance as it happens a lot for non-entailed lexicons.

szpektor and Dagan (2008a) proposed a hybrid method Balanced-Inclusion, BInc, and it was proved to outperform methods proposed prior to it.


In PSL model the number of prediction target grows quadratically with the number of the entities (verbs), if no limitation is provided, which is not desired and is time consuming.

cdd indicates canopies (Mccallum et al., 2000) over the prediction target.


Svm prediction featured by word embeddings Observed entail statement.

it is approximated via , where I * is the most probable interpretation given the current weight (Kimmig et al., 2012).


The probability of generating next word e i is, 2 where t i is a decoding state for time step i, computed by, s i is a source representation for time i, calculated as, where α i,j scores how well the inputs around position j and the output at position i match, computed as, As we can see, NMT only learns an attention (alignment) distribution for each target word over all source words and does not provides exact mutually-exclusive word or phrase level alignments.

it is known that attentional NMT systems make mistakes in over-or undertranslation (Cohn et al., 2016;Mi et al., 2016).


Moreover, POS tags provide useful information for word segmentation.

modelling word segmentation and POS tagging jointly can outperform the pipeline models (Ng and Low, 2004;Zhang and Clark, 2008).


Moreover, different domains means different language usages and contexts.

the In-Vocabulary (IV) word segmentation performance is also affected.


The structures are designed to capture the intuition that nearby sentences in a singledomain corpus often share certain words.

the correlation of characters within or across certain words can be learned, and those involving OOV words notably enhance domain adaption for CWS.


Stopping criterion for Algorithm 1 is SC > θ, where SC is computed as follows: The value of SC is expected to decrease due to a relatively large dip in the value of I(R, S) when Algorithm 1: IB inspired text segmentation Input : Joint distribution: p(R, C), Tradeoff parameter: β Output : Segmentation sequence: Update ∆F (s, s i−1 ) and ∆F (s, s i+2 ); 5 end more dissimilar clusters are merged.

sC provides strong clues to terminate the proposed IB approach.


The IB inspired text segmentation algorithm (Algorithm 1) respects the sequential continuity constraint, as it considers merging only adjacent pairs (see step 2, 3, and 4 of Algorithm 1) while optimizing F ; unlike the agglomerative IB clustering (Slonim and Tishby, 2000).

of this, the proposed IB based approach requires a limited number of involved computations, more precisely, linear in terms of number of text snippets.


We model the set of relevance variables R as word clusters estimated by utilizing agglomerative IB based document clustering (Slonim and Tishby, 2000) where posts are treated as relevance variables.

r comprises of informative word clusters about posts.


Consequently, R comprises of informative word clusters about posts.

each entry p(r i , c j ) in matrix p(R, C) represents the joint probability of getting a word cluster r i in post c j .


Interestingly, the combination of all three clues as happens in the IB:Text+TimeDiff+Poster approach, yielded the best results as all of three segmentation boundaries in ground truth are recovered with high precision.

we submit that the incorporation of non-textual clues is critical to achieve superior results to segment chat text.


The objective of learning is to train the matrix Q = (q 1 , ..., q n ) so as to make q i close to its counterpartq i and also to its adjacent vertices in Ω.

the objective function to be minimized can be written as where α i and β ij are hyperparameters.


This optimization problem is highly prone to overfitting as n ≈ d; moreover the row rank of X is likely lower than n, because the embeddings for cities are highly correlated and thus they are likely linearly dependent.


Note that each ball has an index that distinguishes different occurrences.

the balls of the same color correspond to a sense cluster.


The symbol * denotes that we, instead of using their released codes, carefully reimplement these models for the sake of making the comparisons as fairly as possible.

all the models share the same program switched by the correspondingly parameters (as described in Section 3.3).


This is because "Berry" is used as a person's name much less frequently than a fruit.

it may cause the data sparsity issue, while our model is capable of addressing this problem by improving the usage of training corpus, which will be further discussed in Section 4.5.3.


Because the prior probability is considered to be the source that determines the length of each phoneme segment, embedding this prior into a model is expected to improve the segmentation performance.

we need to construct a model that considers the prior of the segmentation labels and should also reveal the performance of these models for phoneme-level word segmentation.


This was mainly because the phoneme-length context model accelerated the segmentation in the early epochs, and the small number of observations and un-supervised condition caused over-fitting.

the performance of the NPYLM-D is expected to be improved by using a larger corpus or by optimizing hyper parameters to match the actual prior probability of phoneme length.


But there is substantial variance in the performance of CNNs, even on the same folds and with model configuration held constant.

in our experiments we perform replications of cross-validation and report accuracy/Area Under Curve (AUC) score means and ranges over these.


A nice property of sentence classification models that start with distributed representations of words as inputs is the flexibility such architectures afford to swap in different pre-trained word vectors during model initialization.

we first explore the sensitivity of CNNs for sentence classification with respect to the input representations used.


does not have sim-ilar phrases around the coordinating conjunction "and."

existing methods sometimes fail to capture coordination.


As we mentioned in Section 2, we fix the end of the preconjunct at k − 1 and the beginning of the post-conjunct at k + 1.

our model learns and predicts a set of spans (i, j), which indicate the two positions of the beginning and end of a coordination.


Using these scoring functions, we assign scores to all possible pairs of conjuncts.

when the length of a sentence is N and a coordinator appears as the k-th word, we obtain (k − 1) × (N − k) + 1 candidates and choose the pair with the best score as the predicted conjuncts with the softmax function.


(2009), we measure the recall values of coordinate structure boundary prediction, disregarding individual conjunct spans 7 .

we do not decode conjuncts because our model can be compared directly.


However, their method heavily depends on their extension of the Berkeley Parser.

the problem of error propagation between components and the parser still remains.


Our model can capture the connections between conjuncts and other parts of sentences and sentence-level coordination.

our model outperforms existing methods and achieves state-of-the-art performance.


One specificity of distributional thesauri from that perspective is that although the weight between two words is representative of their semantic similarity, we know from work such as (Ferret, 2010;Claveau et al., 2014) that the relevance of the semantic neighbors based on this weight strongly decreases as the rank of the neighbors increases.

our strategy for embedding distributional thesauri is two-fold: first, we build an embedding by relying on methods for embedding graphs, either by exploiting directly their structure or from their representation as matrices; second, we adapt the embedding resulting from the first step according to the specificities of distributional thesauri.


introduced a modification of this model in which the target vocabulary holds a variable number of vectors for each word form, intended to represent its different senses.

the training objective of such a model now has the following shape: the word sense embeddings are trained to maximize the log-probability of context words c i given a word's sense s plus the log-probability of that sense given the word w. For our purposes, this prior is a constant, p(s|w) = 1 n , as we do not have information on the probability of each sense of a given word.


all these triples share the same subject.

we constrain the documents to only have the objects of related triples and also restrict the predicates to be defined only over the objects.


Since available video/text pairs are limited in size, the direct application of end-to-end deep learning is not feasible.

we propose to train Faster R-CNN network for object recognition and LSTM for text generation and combine them at run time.


One of the advantages to choose the cooking domain as a benchmark of procedural text generation from video is that there are a huge number of recipes available on the Web.

it is easy to develop a generative model of recipes for the task.


Some of these sentences may, however, be discarded in the next step.


The dataset contains 180 categories in total, but some categories, for example dish clothes or bottle caps, will not appear in recipe texts.

we ignored such categories and used 95 categories to fine-tune the Faster R-CNN model, which is done in the manner of leave-one-video-out.


These training data are a set of recipe sentences extracted so as to satisfy the following conditions: • The total number of F and T is between 1 and 3, • Each sentence does not contain any r-NE other than Ac, F, and T (see Section 4.4).

the LSTM language model has a tendency not to generate sentences containing 4 or more r-NEs.


Since our task is quite novel and existing end-toend video captioning methods do not obviously work because of lack of large training data, there is no direct baseline.

we discuss absolute BLEU scores of some settings and examples of generated sentences.


The Shannon Entropy of a text T containing a set of characters C is defined as: where and f req(c) is the number of times c appears in T .

mE is the Shannon entropy divided by the text length, len(T ).


However, it's more intuitive for more relevant articles to have more importance.

we construct a model similar to the endto-end Memory Network from Sukhbaatar et al.


In addition, event types contained in documents with the related topics are also consistent.

how to use intra and inter document information becomes particularly important.


As the traditional NMT model is designed for the parallel sentences, it is not suitable for partially aligned sentences.

we redesign the traditional NMT model as follows.


The reason is that the hidden state of last word in source sentence is irrelevant to the target sentence, considering the fact that under our scenario, the target side sentences may entirely uncorrelated to the source sentences.

in our model, z k 0 is set to a zero vector as follows: In Figure 2, the hidden state of "<start>" symbol is set to zero vector when y 1 does not belong to parallel part of the sentence pair.


In the figure, the FPT secutive articles in data set C. These two articles were presented on separate screens.

we split them into two for statistical analysis.


For the MIX one, we also train word embeddings of dimension 50 using Glove (Pennington et al., 2014).

8 we have the embed-dings of plain words, as well as those of token tags.


Still, shortage of memory or lengthy training times prevent us from measuring the full potential of our corpus.

we proposed an entity-type classifier that exploits a set of features computed over an arbitrary large part of WiNER.


The progressive architecture complements information from the ASR network with SER representations trained end-to-end.

in contrast to the fine-tuning model, a progressive network allows learning such low-level emotion-relevant features which the ASR network never learns since they are irrelevant to the speech recognition task.


(Cho et al., 2014a) demonstrated a decrease in the performance of the encoder-decoder model associated with an increase in the length of the input sentence sequence.

introduced attention mechanism to address these issues.


Finally, the decoder task, which predicts the target sequence probability at time t based on previous output and context information c t can be formulated: For speech recognition task, most common input x is a sequence of feature vectors like Mel-spectral filterbank and/or MFCC.

x ∈ R S×D where D is the number of features and S is the total frame length for an utterance.


This might be because the scorer modules are able to correct the details from the Gaussian alignment based on the relevance of the encoder states in the current decoder states.

we conclude that alignment with the scorer is essential for our proposed models.


In the evaluation process, there are some words has multiple references (pronunciations).

we select one of the references that has lowest PER between compared to our hypothesis, and if the hypothesis completely match with one of those references, then the WER is not increasing.


One consequence of this forward propagation of information is that older information tends to fade from the context as new information is integrated into the context.

rNN-LMs struggle in situations where there is a long-distance dependency because the relevant information from the start of the dependency has faded by the time the model has spanned the dependency.


This difference is important because in the wikitext2 dataset the sentences are not shuffled and, therefore, are sequentially related to each other.

systems that carry sequential information from previous sentences into the current sentence have an advantage in that they utilise contextual cues from the preceding sentence to inform the predictions at the start of the new sentence.


This way, the models do not let information fade away from the context as it progresses to subsequent steps in a sequence and all previous information about the words that preceded the current timestep is available to the classifier in a manner that disregards recency.

of the smoothing effect, the model does not necessarily need to store information about the context of the sequence in the recurrent connections of the RNN.


This behaviour enable the model to retrieve information from the buffer to remember past words without relying solely on the RNN's internal "memory".

the model can maximize the features extracted about an input word, creating an advantage over other RNN-LMs that need to both extract features and keep context regarding the sequence in its connections.


Whereas inter-feature dependencies are synchronic in nature, inter-language dependencies reflect diachrony.

we call the integrated model diachrony-aware induction.


The same holds for the most horizontally diffusible ones.

we can conclude that the induced representations retain ver-tical and horizontal signals observed for surface features.


In addition, dynamic updates of both the input and output layers (D) further improved the performance from those of either the output (C) or input (B) layer.

the proposed dynamic output was shown to be compatible with dynamic input, and vice versa.


The use of the latest context had the worst performance for all variants of the proposed method.

a proper accumulation of multiple contexts is indispensable for dynamic updates of word embeddings.


Implicit relations in the PDTB are only ever annotated between consecutive sentences.

we specifically extract pairs of consecutive sentences on the source English side: • that are identified as the Arg1 and Arg2 of an implicit discourse relation 1 ; • whose corresponding back-translated target sentences are identified as the Arg1 and Arg2 of an explicit relation; • that are not part of the Arg1 or Arg2 of any other discourse relations 2 .


Despite of the superior ability of the deep learning models, the syntactic information, which proves to be helpful for identifying discourse relations in many early studies (Subba and Di Eugenio, 2009;Lin et al., 2009), is seldom employed by recent work.

we are curious to explore whether such missing syntactic information can be leveraged in deep learning methods to further improve the semantic modeling for implicit discourse relation classification.


For example, considering a node tagged with NP, its child node tagged with DT is usually neglectable.

we propose to incorporate this tag information into the tree-structured neural networks, where those constituent tags can be used to control the semantic compostion process.


For example, for most of the time, constituents with PP (prepositional phrase) tag are less important than those with VP (verb phrase) tag.

we argue that these tags are worth considering when we compose the semantics in the tree-structured neural networks.


Furthermore, these gates are computed dynamically according to the inputs at a certain step.

it's natural to incorporate the tag embeddings in the computation of these gates.


Moreover, the green tags are, which are located in the right-bottom corner, all describe the clause-level constituents.

from this visualization, we can conclude that the tag embeddings we learned are somewhat meaningful and really capture some functionalities of these tags.


Methods for sentiment analysis and classification have largely been limited to English, making use of large amounts of labeled data to produce sentiment classification.

many developed approaches cannot be readily applied to other languages, which usually do not have the wealth of labeled data that is exclusive to English.


The second sentence has a negative feel to it from the accuracy (bug) feature (aspect).

a simple linear aggregation (sum of the individual polarities) of the features, will yield a overall neutral review polarity score.


However, there is a limitation of SKIP-GRAM word embeddings: Contexts need not correspond to all of the words and the number of context-types maybe larger than the number of word-types.

dependencybased contexts capture more information than bagof-words contexts.


For example, Australian is not used as the context for discovers.

we have more focused embeddings that capture more functional and less topical similarity.


Moreover, the advertisers are only interested in the target class.

it is more suitable to model as a oneclass classification problem, in contrast to traditional classification problems where disjoint classes are defined a priori.


In practice, we cannot simply perform up-or downsampling without the labels of the documents.

we did not try to modify the class ratio to make it more balanced.


In the real-world scenario, the keywords will be composed by non-technical users, instead of researchers in NLP who are familiar with the algorithm.

we cannot make assumptions of the quality of the keywords the user composes.


Identifying the references to these special entities in text is a crucial step toward Language Understanding.

there have been many works on these areas.


We extended this idea treating the biases as parameters.

the trainable bias decoding system has the number of parameters equal to the number of classes.


The RNN, trained on a large corpus of unlabeled data, is adept at identifying the strength of semantic relationships between messages.

it is utilized as a feature in every classifier of the pipeline.


Then we build a supervised classifier to classify the remaining Word-Net verbs.

we can drastically cut down on the number of verbs to be annotated manually.


Unlike the word level, each sentence is relatively independent of other sentences and takes its own part in determining the document sentiment.

we ignore modeling the dependence between sentences and directly use the sentence representations derived from the word level.


This conforms to our intuition: some function words and punctuation marks contribute little to sentiment classification, but different words are focused on from different views.

multiple ways of using user and product information can well gather information which is helpful to representing a sentence.


As we can see in the development set, there are some unambiguous cases.

we add more information to CNN sentiment vectors by concatenating them with LSTM sentiment vectors or DVngram semantic vectors.


However, similar sentences/documents have a tendency to be encoded into similar vectors by generative models.

it causes some difficulties in sentiment classification.


On document level, LSTM method produced a much lower performance than DVngram method.

the feature vectors generated from LSTM model does not support as well as DVngram's vectors when combining with CNN feature vectors.


Semantic information maybe provides opposite opinions in different contexts.

some research (Socher et al., 2011;Tang et al., 2014) worked on learning sentiment-specific word representation by employing sentiment text.


In this case, we could only consider the main nouns (i.e., "bar" and "church"), but that approach would cause difficulties when dealing with other phrases such as sell a car and drive a car, which both involve an automobile but describe dissimilar actions.

successful systems should be able to properly focus on the most semantically relevant tokens with a phrase.


Rather, the systems should be evaluated based on their ability to rank the set of phrases in the same order as the ranking given by the average human annotations scores for each dimension.

we calculate the Spearman Rank correlation between scores given by the automated systems and the human judges our final score for each system.


Although it is easier to build large generaldomain comparable corpora, specialized compa-rable corpora are often of modest size (around 1 million words) due to the difficulty to obtain many specialized documents in a language other than English.

word co-occurrence counts of the historical context-based projection approach, known as the standard approach (Fung, 1995;Rapp, 1995), dedicated to bilingual lexicon extraction from comparable corpora are unreliable in specialized domain.


First, we recall that the order of edit operations called σ k inside the sequence δ (i) is partially determined by their elements.

it can be shown, more generally that the order restriction in (7), (8) leads directly to where the order between any two edit operations in δ (i) , called σ k , σ l is free only in the case of multiple insertion acting on the same position of a string, i.e.


the indexing function h defined in 9 forms a strictly increasing series.


The tone marker filter removes all character deletions and insertions in code segments.

tonalization is modeled by only tone marker insertions as below: Here, we found a tonalization representation similar to the one from the preceding example: insertion of a tone marker at position 2 for some syllables.


Otherwise it is likely to be a Statement.

dA recognition requires us to understand the sentence from semantic, pragmatic and syntactic aspects, and its context plays an important role as well.


Especially for spoken dialog systems, NLU is based on Automatic Speech Recognition (ASR) hypotheses or transcripts, in which we cannot make any assumption of punctuation such as periods, commas and question marks for segmentation.

dA segmentation becomes essential for spoken dialog systems.


Let s t i denotes the t-th element of s i , the score of a tag sequence t 1:L is defined as: and we use the Viterbi algorithm to find the optimal tag sequence t * 1:L that maximizes the sequence score: DA segmentation splits a sequence of words into segments, and DA recognition assigns a dialog act type to them.

these two tasks are naturally conducted in a cascading manner.


In the task of DA recognition, we evaluate the model performances of DA recognition.

we directly use ground-truth segments as inputs and predict a DA label for these segments.


Similar to aforementioned conclusion, joint training helps the joint training model outperform the cascading model by 0.8% in accuracy, 0.9% in Macro F1 measure and 0.7% in Weighted F1 measure.

we can conclude that joint training can also learn features from segmentation task to help recognize DA tags.


However, it is impractical to ask users to provide explicit feedbacks when the agents' responses displease them.

in this paper, we explore to predict users' imminent dissatisfactions caused by intelligent agents by analysing the existing utterances in the dialogue sessions.


As shown in Figure 1, the emotional conflict between the last agent's response and the user's mood leads to the dissatisfaction.

the negative feedback from user could be predicted according to the context utterances.


However, in this dissatisfaction prediction task, the user is not only the reader of the session text, but also the writer of some utterances.

some clues of the particular user's emotion may be contained in the context.


However, traditional RNNs are not adequate to represent such consistency.

we attempt to model the sequence of relation between utterances.


Finally, 30,034 sessions meeting   The manually labelled gold standard dataset might be insufficient for learning deep neural models.

a distance supervision strategy is designed to obtain augmented data.


This is due to the fact that the gated operation makes it possible to adjust sentence representation according to R 0 .

besides the interaction between adjacent utterances handled by the recurrent structure, the influence of interaction between R 0 and other utterances can be involved into the final representation and distance relation and consistency could be encoded.


Both Q −2 and Q −1 contains negative emotions and possibly lead to dissatisfaction.

the probabilities of negative feedback increase at the corresponding time step.


We compare the performance between two types of agents and find that the RL-based agent has greater robustness and is less sensitive to noisy inputs.

the following experiments are performed using a RL dialogue agent due to robustness consideration.


It is also common to distinguish between verbatim quotes, mixed quotes, and paraphrases (also known as reported speech) (Sundar, 1998).

we create a baseline model of verifiabilty based on these features.


Direct quotes also appear to enhance credibility compared with paraphrases (Sundar, 1998).

re-searchers have recently tracked the use of anonymous sources over time and across cultures (Esser and Umbricht, 2014;Lee and Wang, 2016).


As discussed above, methods for extracting attributions from raw text have been developed in prior work.

this task focuses on the regression of perceived verifiability from text that has already been annotated with attributions in PARC3 annotation style.


Whenever a source was mentioned using a personal pronoun, we interpolated the pronoun using the representative coreferent mention, except where that mention already occured in the sentence.

for example, "I don't know," she said, might become "I don't know," Lindsay Walls, CEO of Inovatron said.


In cases where NLP is used to address social science problems, the training data is often unavailable when it comprises personally identifiable information, or when sharing would violate privacy and ethical regulations.

an appropriate unsupervised domain adaptation approach would expect only a trained model -not the original data -to be available for predicting outcomes.


Facebook requires consenting users to share their data, but while obtaining consent, it is easy to ask users questions to assess their personality, or to ask them to share other data such as their electronic medical records (Smith et al., 2017).

we face the technical question: How can we take a model trained to predict user attributes such as personality from Facebook language at the individual user level and use it to predict average personality from Twitter language at the county level?


(Nobata et al., 2016) revealed that only 5.9% of online comments contained hate speech.

large amounts of online texts need to be annotated to adequately identify hate speech.


Seeing a hate slur term in a tweet strongly indicates that the tweet is hateful.

we use 20 manually selected slur terms to match with a large unlabeled tweet collection in order to quickly construct the initial small set of hateful tweets.


While we were aware of other more sophisticated machine learning models, one purpose of this research is to detect and learn new slur terms from constantly generated user data.

the simple and clean string matching based slur learner is designed to attentively look for specific words that alone can indicate hate speech.


This demonstrates that hateful tweets often contain both explicit hate indicator phrases and implicit expressions.

in our two-path bootstrapping system, the hateful tweets identified by slur matching are useful for improving the LSTM classifier, vice versa.


In order to understand how necessary it is to maintain two learning paths for online hate speech detection, we also ran two experiments with one learning component removed from the loop each time.

the reduced bootstrapping systems can only repeatedly learn explicit hate speech (with the slur learner) or implicit hateful expressions (with the LSTM classifier).


In parallel, recent advances in generative text modeling have demonstrated the effectiveness of recurrent neural networks in capturing content, structure, and style in natural language.

several recent works have focused on learning generative models of product reviews, either to generate reviews per se, or as a means of learning user and item attributes (Lipton et al., 2015;Radford et al., 2017;Dong et al., 2017;.


In particular, we focus on estimating latent user preferences through implicit feedback while simultaneously predicting the contents of the reviews themselves.

given an item that a user hasn't yet interacted with, our goals are to (a) generate a plausible review, in order to estimate the user's nuanced reaction to the product; (b) estimate whether the user would be likely to interact with the product, based on their learned latent attributes; and (c) rank existing reviews using the language model, in order to surface 'meaningful' reviews to the user.


On the CQA site, users can freely write a question text and its title.

some of the pairs in the data can be regarded as questionsummary pairs, but some others cannot.


Furthermore, it is not clear how questions are summarized, i.e., whether by extractive or abstractive methods.

in this study, we first analyzed the data to clarify the following issues: 1.


refers to the program name "the simpsons", which appeared previously.

the summary does not contain enough information to grasp the gist if we naively apply any extractive approaches.


However, in the question summarization, the summaries should be also questions.

we adopted methods to select a question in the input by heuristic rules, choosing the first question (Lead Question) and the last question (Last Question).


In summarization tasks, the words in the input are often likely to appear in the summary.

we also adopted the encoder-decoder model with the copying mechanism (Gu et al., 2016), which can select words in the input as words in the output.


We separate the remaining equally for the development and the test set.

we split the dataset into 18 (train):1 (development):1 (test).


However, we assumed that a better summary would contains more focused content in a shorter output.

as the evaluation metric, we adopted the ROUGE-2 F-measure in addition to Recall.


(2015) mentioned, encoder-decoders models without an attention do not work well for long sentences.

the model failed to decode the sequence.


In terms of grammaticality, some Human summaries are not complete sentences such as "The best way to get money?".

human was sometimes judged lower than CopyNet.


This approach still runs for several hours on large problem instances due to the expensive calculation of SCORE (lines 11-12), making more complete local searches, using best-first or beam search, impractical.

we obtain a relation S that partitions M into a set of sets C = {C 1 , .


The additional constraints ensure that only one virtual edge can be active (12), that the virtual node can only send flow over this active edge (11) and that the total amount of flow sent from the root cannot exceed the size of the selected subgraph (13).

if n concepts are selected, n units of flow are sent from the root over the edges of the graph and each selected concept consumes one of them.


The weight of an edge in a syntactic dependency in the input corpus should represent its topical relevance for summary generation.

the weight depends on the frequency of bigram constituted by words on either side of the dependency edge(D bigram ).


Syntactic complexity is a crucial factor for argument identification, both in terms of overall performance as well as and in terms of individual argument type performance.

it is inevitable to reduce or better handle syntactic complexity.


This eases the burden of the decoder by shifting the labeling task to the selector.

our model requires a smaller vocabulary for the target language.


Although here Joint Seq2Seq gets the better BLEU score against the incomplete reference, Selective Decoding is able to better generlize over the train data: the facts it generates are much closer to the original input sentence, and even better than the reference.

结果 , 民主党 失去 了 列举 布什 " 罪状 " 的 良 机 , (the Democratic party lost a good opportunity to list the ' charges ' against Bush.


We define the first argument as the more important argument.

we can regard v i as the first argument of the argument pair ( If m ij and m ji are equal, v i and v j have similar argument roles.


Previous approaches (Spagnola and Lagoze, 2011;Guo et al., 2011) consider the distance between two nodes and the influence of each node modeled by its weighted frequency to measure the strength of links in networks.

similarly, in our setting we can regard c ij as the distance between v i and v j and use the relative importance score to measure the influence of each node in G. we obtain Equation 5 to compute the relation strength F (i, j) between nodes v i and v j .


The associated weighted context words of each candidate argument pair are not sufficiently informative and flexible to clearly express the relation between two arguments.

we aim to name the relation between a pair of arguments as one of the KB relations or NONE by comparing the semantic representations of context words and KB relations based on word embeddings.


We calculate the absolute value since we have already captured the direction of arguments in Section 2.2.

we can regard similarity scores −1 and 1 equally and 0 as the lowest score.


(2016) found that NAT2 gene is associated with breast cancer when women with NAT2 gene polymorphisms have been exposed to long-period active smoking.

active smoking has been considered as a crucial factor that determines the relation between NAT2 gene and breast cancer, which biologists called gene and environment interaction (GxE).


There are four types of genes (MTNR1a, MTNR1b, AANAT, insulin) and two types of diseases (breast cancer, cancer).

we consider twelve gene-disease combinations for which our system attempts to find environment terms from the abstract.


In the case of the static RNN decoder, it is necessary to set the length of the model due to a static attribute.

we evaluated the performance of the model according to the length, and we found that the model with a length of 25 performs best.


Among them, SVM outputs skewed results, failing to find any environment terms, and classifying all test data to '<NOE>'.

its performance depends on the number of input data showing no environment terms, which explains why the model shows even precision and recall.


But, the overall difference in performance is much bigger because the average of the numbers in the corpus is nearly 5.

the static RNN decoder outperforms other models.


Unless considering the combination, it seems hard to identify environment terms due to many negative examples.

given a combination, (RGS4 and fetal hypoxia), without an environment term such as the example in Table 5, the static RNN decoder using a basic encoding seems to regard the majority of combinations including the combination with environment as a negative example, as shown in the first column of Table 5.


In other words, high-confidence course concepts in CCG could propagate their confidence scores to their neighbor nodes that have high semantic relatedness with them, to discover other potential course concepts.

we propose an iterative graph-based algorithm, namely course concept propagation (CCP), which first assigns each vertex of CCG an initial confidence score, and iteratively updates the score for each vertex through propagation.


Based on the above questions, we define two general functions as follows: a) voting score vs k (c j , c i ): determines the confidence score c j propagates to c i in the k-th iteration; b) voters A(c i ): it specifies the vertex set from which c i receives the voting scores in each iteration.

a general propagation process can be defined as follows.


For each candidate, the annotator is asked to make a judgement about whether it is a course concept based on the course contents.

each dataset is doubly annotated, and pearson correlation coefficient is applied to assess inter-annotator agreement.


Only one or two sentences may not be adequate for this purpose.

in the following experiments, we only use Description for system comparisons.


We use exact sentences from a clinical narrative as queries to search for the diagnoses in the knowledge sources.

sometimes our system is not able to identify the correct diagnosis due to noise in the query (see Table 6).


This is an aspect that we did not take into consideration when training the baseline model.

the model is not explicitly provided with the database schema, however it can infer it from the training set.


However, there are cases when the rating of CG generated statements decreases for one or both of the considered cultures when compared to the HG statement.

it might be beneficial to consider both nationality as well as the topic of the original statement during the generation of system action variants.


For elaborateness, we could show that different preferences exist depending on the culture and original statement.

it is feasible for an adaptive DM to choose either the elaborated or the concise variant depending on both the culture of the user as well as the current context of the conversation to improve the user satisfaction.


However, this information is not accessible for most tweets.

we propose different text processing methodologies to compute the three demographic variables: age, gender and location.


One final model was proposed that joins demographicagnostic word embeddings with demographic dicrete variables.

15 different input settings were tested for classification as shown in the first column of Table 4, using the linear SVM model implemented in the Weka 13 platform with standard parameters settings 14 .


Although the English unsupervised approach assumes that there are explicit word segmentations, conventional analyzers often fail to segment non-standard words in Japanese.

to extract variants in an unsupervised fashion, we have to introduce an idea to generate correct word segmentation of variant words.


We use training data for morphological analysis in the second step but do not use annotated data in the first step.

we can efficiently extract many types of variantnormalization pairs that appear in real text.


Although a more recently proposed model PV can directly train vector representations for short texts such as movie reviews (Le and Mikolov, 2014), our experiments in Section 5.3 show that PV is not appropriate for standard paragraphs in general documents.


We however show that the test statements in the True-or-False questions have compound characteristics through an analysis of past exams ( §3).

direct conversion alone does not suffice for solving this task satisfactorily, because each method is built on its own problem setting, which does not fully cover the variety of the test statements, especially the false ones.


The second step is to find the subject s with high ( | , ).

the object in the KB triple which contains the subject s and the relation r with the highest ( , | ) is the answer.


Another reason why people communicate is to strengthen their social bonding with others.

daily conversations are rich in emotion.


While the sentence-paraphrase pairs that are labeled entailed and rated 3.0 and above are usually grammatically correct, the sentences with an average rating below 3 and labeled entailed sometimes contain a grammatical errors, and some are rated neutral or contradictory.

we remove sentences with an average rating less than or equal to 3.0 and greater than 2.5.


Each DPR coreference problem-solution pair can be converted to two annotated entailment problems by substituting the target pronoun with the two expressions that it could corefer with.

two RTE pairs are generated for each DPR pair: one that is entailed and one that is not entailed.


We believe this is because the sentences in FN+ contain language that is rarely seen.

they contain more subtle differences compared to the differences between the text and the hypothesis in the SNLI dataset.


Above the level of individual contributions to discussion, the notion of a conversational role is relevant both for characterizing the rights and responsibilities an individual has within an interaction as well as the configuration of conversational behaviors the person is likely to engage in.

it is not surprising that prior work has revealed that the process of becoming a Wikipedia moderator is associated both with changes in language use and in the roles editors play on the talk pages (Danescu-Niculescu-Mizil et al., 2012).


Their models consist of serially connected multi-layer CNNs for encoders or decoders, similar to image recognition CNNs for 1D image processing.

their models do not have any direct mechanisms for dealing with the connections between phrases/words in long distance.


An isolated word on its own does not mean anything in the word vector space.

one needs to think of embedding algorithm's capability to capture these differences effectively, which is governed by its hyperparameters.


To further verify the distortions due to a lower than needed dimension, we make the following hypothesis: if the learning algorithm of word embeddings does not get enough dimensions, then it will fail to uphold the equality constraint.

the standard deviation of the mean of all pairwise distances will be higher.


As we increase the dimension, the algorithm will get more degrees of freedom to model the equality constraint in a better way.

there will be statistically significant changes in the standard deviation.


The 3 dimensional vectors in (c) and (f) are reduced to 2 dimensions using PCA for visualization purposes bound of dimensions is reached, the algorithm gets enough degrees of freedom.

from this point onwards, even if we increase dimensions, there will not be any statistically significant difference in the standard deviation.


This is because once the lower bound is reached, the errors introduced due to the violation of equality constraint are removed.

the optimal performance possible with the selected configurations is reached, and the performance stabilizes thereafter.


Then, for each 10 sentence pairs, the 5th and 10th pairs are used for the development and test sets respectively, and 8 remaining pairs are used for the training set.

each of the development and test sets has 2,915 pairs, and the training set has 23,320 pairs.


For example, a word followed by a preposition such as 'of' is probably a noun.

if the word at timestep t is a preposition, IOG refines the output of a language model to raise the probabilities of nouns.


Let us look at a toy example: our training data is such that whenever the label mountain is active then the label tree is also active.

mLL algorithm should take advantage of this correlation embedded in the training data to always infer the label tree when mountain is one of the label.


The tf-issf approach also does not require context words since we compute the term frequency of each katakana term in question instead of the frequency of its context words.

we need not assume that the training corpus has been automatically segmented by an existing to-kenizer, which might include segmentation errors involving context words.


In WS, the values of both RN in the reference as well  as PN by the system are drastically greater than the corresponding values of the positives.

tN is high regardless of how the segmenter performs, which makes tNR very insensitive and inappropriate as an evaluation metric for WS.


Another large scale dataset, containing user generated image descriptions for 1M images, created by quering Flickr.

the captions are not necessarily full or accurate (see Figure 1).


On the other hand, as exemplified in Table 4, HAS and some other features can detect the linkages between complex words and their simpler counterparts.

of properly capturing the alignments between such lexical paraphrases, our system successfully classified this sentence into good in terms of overall quality.


(Faruqui et al., 2015) set α as 1 and β as 1 γ where γ is the number of vectors to be fitted with.


Collecting sufficient data to train ASR systems using current state of the art methods, such as deep neural networks (Graves and Jaitly, 2014;Xiong et al., 2016), is difficult.

it is common to use welltrained, cloud-based ASR systems.


Speech and gesture information within the same semantic category can then be fused to form a complete multimodal meaning, where previous methods on representing multimodal semantic (Bergmann and Kopp, 2008;Bergmann et al., 2013a;Lascarides and Stone, 2009; Giorgolo, 2010) can be applied.

this enables HCIs to construct and represent multimodal semantics of natural communications involving iconic gestures.


Annotations with more than 1 token were split into a sequence of tokens (e.g., BAB/BUP to BAB, BUP).

gesture feature sequences have variable lengths, in the same sense as utterances have variable amount of word tokens.


For example, the temporal relation in Example (A) is annotated with three signal labels in the RST Signaling Corpus: 1 (1) discourse marker (now) (2) tense (past -present, future ) (3) lexical chain (first year -next year) Only 7% of the relations are annotated as 'implicit'.

most conventionally 'implicit' relations are also annotated with explicit signals and included in the present analysis.


We focus on relations that are signaled by surface tokens in order to examine word-level incrementality in discourse processing.

we do not consider signals that are not associated with particular words, e.g.


Each role-dependent recurrent unit BLSTM rolex receives corresponding inputs x i,rolex (i = [1, ..., N ]), which have been processed by an encoder model, we can rewrite 5and 6into 7and (8) respectively: + BLSTM role b (intent t,role b ).

v his = BLSTM rolea (CNN(utt t,rolea )) (8) each role-based contextual module focuses on modeling the role-dependent goal and speaking style, and v cur from (2) is able to carry role-based contextual information.


The reason probably is that the guide has similar behavior patterns such as providing information and confirming questions etc., while the user can have more diverse interac-tions.

understanding the user intents is slightly harder than predicting the guide policy in the DSTC4 dataset.


MMR chooses the next candidate s * such that (3) where λ is a hyper-parameter balancing these two aspects.

the fine-grained selection improves the diversity and retains the quality of subsequences at each time of decoding, so the generated replies are of good quality and diversity.


For example, important sentences are likely to appear in front of a newspaper article.

we adopt positional information as a main feature.


It is known that the categorical QLMs do not perform well for vocabulary mismatches because categorical distribution cannot consider semantic relationships between words.

several expansion techniques such as query expansion (Bai et al., 2005), translation QLMs (Berger and Lafferty, 1999), and latent variable models (Wei and Croft, 2006) have been proposed in order to take semantic relationships between document and target query into account.


P (w|v) represents the probability of translating word v into word w. P (w|v) is heuristically calculated as where sim(w, v) is the word similarity between w and v. In order to calculate P (w|v), cosine distances between pre-trained word embeddings were recently utilized (Zuccon et al., 2015;Ganguly et al., 2015).

the word similarity is calculated as where w is the word embedding normalized to a unit length for w. This paper proposes a novel probabilistic IR method based on hyperspherical QLM that leverages pre-trained word embeddings as random variables for directional probabilistic models.


In order to introduce a mixture of von Mises-Fisher distributions to hyperspherical QLMs, unified assumptions are essential for each document modeling because the hyperspherical QLMs are utilized for IR.


In fact, mixture weights are estimated as ML or MAP estimated values in categorical QLMs.

in ML estimation,α m estimated from document D is determined asα ML estimated generative probability of w in the hyperspherical QLMs is formulated as where v is a normalized word embedding of v. in MAP estimation,α m estimated from document D is determined aŝ where C is document collection in which all retrieved documents are included.


In ML estimation,α m estimated from document D is determined asα Thus, ML estimated generative probability of w in the hyperspherical QLMs is formulated as where v is a normalized word embedding of v. In MAP estimation,α m estimated from document D is determined aŝ where C is document collection in which all retrieved documents are included.

mAP estimated generative probability of w in the hypersherical QLms is formulated as The hyperspherical QLms can be interpreted as an extended form of categorical QLms.


Candidate Pruning: Calculation of score for all triplets from the dataset is an memory and time wise prohibitive operation.

at first we prune the facts to generate candidate facts similar to (Jain, 2016).


We take the root to be at level zero, with finer types having higher levels.

pairs with fine-grained types (like Physicist), and with high prediction confidences, accumulate higher scores.


Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts.

we extract dependency chains containing context events and use them as input in neural network models, which consistently outperform previous models using local context words as input.


This model converts the sentence by using character phrase units.

we call this method "character-level conversion".


Obviously, the augmented data are less reliable than human-annotated data.

we can expect to improve the performance of the normalization model by ignoring the less reliable augmented data in the last-mile decision of model parameter training.


Data parallelism and model parallelism are two commonly used techniques to improve the speed of training .

multiple GPUs or T-PUs 1 are needed which requires additional replication and combination costs.


Sentences with more than 50 words from the training data are deleted, and the parallel documents that comprise continuous sentences are collected.

the number of sentences in the training data is 1,103,336 (329,025 documents; the average number of sentences per document is 3.35).


We also use the NTCIR Corpus (Goto et al., 2013) to investigate the corpus-specific tendencies.

of the preprocessing used with the NTCIR, the number of sentences in this training data is 1,169,201.


Note that we could not reconstruct this corpus at a documentlevel one.

our voice classifier only uses the sentence-level features in the experiments of voice prediction experiments.


We also show that combining multiple related language pivot models can rival a direct translation model.

the use of subwords as translation units coupled with multiple related pivot languages can compensate for the lack of a direct parallel corpus.


Linear interpolation of the direct system with all the pivot systems with equal interpolation weights also benefitted the translation system.

multilinguality helps overcome the lack of parallel corpora between the two languages.


Furthermore, we perform experiments with a conventional NMT framework, in which a part of the initial value of a weight matrix is set to zero so that the matrix is at the same initial-state as the key-value attention mechanism.

we obtain comparable results with the key-value attention mechanism without changing the network structure.


The encoder embeds the source sentence x into vectors through an embedding matrix and produces the hidden states using a bidirectional RNN, which represents a forward and a backward sequence.

we have K×Vs is an embedding matrix where K is the word embedding size, and enc 1 and enc 2 are nonlinear functions as in LSTM.


However, the requirement to compress all necessary information into a single memory vector in each memory slot is likely to cause performance deficiencies.

to alleviate this problem, Miller et al.


Then, the impact of cross-entropy increases and the model moves to the true optimal determined by the entropy.

if the optimal distance is sufficiently small, then this method will guide the training in early updates and preserve the true optimal with respect to cross entropy with restriction of generating negative sentences.


In the case of training accuracy, the decoder assumes receiving correct input at every time step while it uses previously selected word in actual decoding.

in actual decoding to generate translation for the test set, a selected wrong word in an intermediate step may cause subsequent errors by strange context generation.


We conjecture that, as a consequence of these structural properties, these corpora don't fully represent the complexity of code documentation and code generation as typically done by human programmers, and may be thus of limited use in practical applications.

we identify the need for a more challenging corpus that better represents code and documentation as they occur in the wild.


Current approaches to sequence transduction work best on short and ideally independent fragments, while source code can have complex dependencies between functions and classes.

we only extract top-level functions since they are usually small and relatively self-contained, thus we conjecture that they constitute meaningful units as individual training examples.


This enables us to take the values of easy-to-predict properties into account when predicting the values of difficult-to-predict properties.

joint prediction can boost predictive accuracy.


In addition, Skip-grams are known to be more accurate in large datasets.


In this paper, we construct new corpus with nxml documents downloaded from PubMed site.

of extracting the nxml document, a total of 807,821 papers were extracted, and the titles and abstracts were collected separately and used as a corpus for word embedding.


In other words, the larger the difference between the values of BEST and WORST, the more favorable it is.

the BEST marker values of CCA were lower than those of the WORST markers.